csplit

 

NAME

csplit − split files based on context

SYNOPSIS

csplit [-ks][-f prefix][-n number] file arg1 ...argn

DESCRIPTION

The csplit utility shall read the file named by the file operand, write all or part of that file into other files as directed by the arg operands, and write the sizes of the files.

OPTIONS

The csplit utility shall conform to the Base Definitions volume of IEEE Std 1003.1-2001, Section 12.2, Utility Syntax Guidelines.

The following options shall be supported:
-f 
prefix

Name the created files prefix 00, prefix 01, ..., prefixn. The default is xx00 ... xx n. If the prefix argument would create a filename exceeding {NAME_MAX} bytes, an error shall result, csplit shall exit with a diagnostic message, and no files shall be created.

 

-k

 

Leave previously created files intact. By default, csplit shall remove created files if an error occurs.

-n  number

Use number decimal digits to form filenames for the file pieces. The default shall be 2.

 

-s

 

Suppress the output of file size messages.

 

OPERANDS

The following operands shall be supported:

 

file

 

The pathname of a text file to be split. If file is ’-’ , the standard input shall be used.

The operands arg1 ... argn can be a combination of the following:
/rexp/[offset]

A file shall be created using the content of the lines from the current line up to, but not including, the line that results from the evaluation of the regular expression with offset, if any, applied. The regular expression rexp shall follow the rules for basic regular expressions described in the Base Definitions volume of IEEE Std 1003.1-2001, Section 9.3, Basic Regular Expressions. The application shall use the sequence "\/" to specify a slash character within the rexp. The optional offset shall be a positive or negative integer value representing a number of lines. A positive integer value can be preceded by ’+’ . If the selection of lines from an offset expression of this type would create a file with zero lines, or one with greater than the number of lines left in the input file, the results are unspecified. After the section is created, the current line shall be set to the line that results from the evaluation of the regular expression with any offset applied. If the current line is the first line in the file and a regular expression operation has not yet been performed, the pattern match of rexp shall be applied from the current line to the end of the file. Otherwise, the pattern match of rexp shall be applied from the line following the current line to the end of the file.

%rexp%[offset]

Equivalent to /rexp/[offset], except that no file shall be created for the selected section of the input file. The application shall use the sequence "\%" to specify a percent-sign character within the rexp.

line_no

Create a file from the current line up to (but not including) the line number line_no. Lines in the file shall be numbered starting at one. The current line becomes line_no.

 

{num}

 

Repeat operand. This operand can follow any of the operands described previously. If it follows a rexp type operand, that operand shall be applied num more times. If it follows a line_no operand, the file shall be split every line_no lines, num times, from that point.

An error shall be reported if an operand does not reference a line between the current position and the end of the file.

STDIN

See the INPUT FILES section.

INPUT FILES

The input file shall be a text file.

ENVIRONMENT VARIABLES

The following environment variables shall affect the execution of csplit:

 

LANG

 

Provide a default value for the internationalization variables that are unset or null. (See the Base Definitions volume of IEEE Std 1003.1-2001, Section 8.2, Internationalization Variables for the precedence of internationalization variables used to determine the values of locale categories.)

 

LC_ALL

 

If set to a non-empty string value, override the values of all the other internationalization variables.

LC_COLLATE

Determine the locale for the behavior of ranges, equivalence classes, and multi-character collating elements within regular expressions.

LC_CTYPE

Determine the locale for the interpretation of sequences of bytes of text data as characters (for example, single-byte as opposed to multi-byte characters in arguments and input files) and the behavior of character classes within regular expressions.

LC_MESSAGES

Determine the locale that should be used to affect the format and contents of diagnostic messages written to standard error.

NLSPATH

Determine the location of message catalogs for the processing of LC_MESSAGES .

ASYNCHRONOUS EVENTS

If the -k option is specified, created files shall be retained. Otherwise, the default action occurs.

STDOUT

Unless the -s option is used, the standard output shall consist of one line per file created, with a format as follows:

"%d\n", <file size in bytes>

STDERR

The standard error shall be used only for diagnostic messages.

OUTPUT FILES

The output files shall contain portions of the original input file; otherwise, unchanged.

EXTENDED DESCRIPTION

None.

EXIT STATUS

The following exit values shall be returned:

 

  0

 

Successful completion.

 
 

>0

 

An error occurred.

 

CONSEQUENCES OF ERRORS

By default, created files shall be removed if an error occurs. When the -k option is specified, created files shall not be removed if an error occurs.

The following sections are informative.

APPLICATION USAGE

None.

EXAMPLES

 

1.

 

This example creates four files, cobol00 ... cobol03:

 

csplit -f cobol file ’/procedure division/’ /par5./ /par16./

After editing the split files, they can be recombined as follows:

cat cobol0[0-3] > file

Note that this example overwrites the original file.

 

2.

 

This example would split the file after the first 99 lines, and every 100 lines thereafter, up to 9999 lines; this is because lines in the file are numbered from 1 rather than zero, for historical reasons:

csplit -k file 100 {99}

 

3.

 

Assuming that prog.c follows the C-language coding convention of ending routines with a ’}’ at the beginning of the line, this example creates a file containing each separate C routine (up to 21) in prog.c:

csplit -k prog.c ’%main(%’ ’/^}/+1’ {20}

RATIONALE

The -n option was added to extend the range of filenames that could be handled.

Consideration was given to adding a -a flag to use the alphabetic filename generation used by the historical split utility, but the functionality added by the -n option was deemed to make alphabetic naming unnecessary.

FUTURE DIRECTIONS

None.

SEE ALSO

sed , split

COPYRIGHT

Portions of this text are reprinted and reproduced in electronic form from IEEE Std 1003.1, 2003 Edition, Standard for Information Technology -- Portable Operating System Interface (POSIX), The Open Group Base Specifications Issue 6, Copyright (C) 2001-2003 by the Institute of Electrical and Electronics Engineers, Inc and The Open Group. In the event of any discrepancy between this version and the original IEEE and The Open Group Standard, the original IEEE and The Open Group Standard is the referee document. The original Standard can be obtained online at http://www.opengroup.org/unix/online.html .

基于 NSFW Model 色情图片识别鉴黄 后面更新视频检测 项目背景: 随着互联网的快速发展,网络上的信息量呈现出爆炸式的增长。然而,互联网上的内容良莠不齐,其中不乏一些不良信息,如色情、暴力等。这些信息对青少年的健康成长和社会风气产生了不良影响。为了净化网络环境,保护青少年免受不良信息的侵害,我国政府加大了对网络内容的监管力度。在此背景下,本项目应运而生,旨在实现对网络图片和视频的自动识别与过滤,助力构建清朗的网络空间。 项目简介: 本项目基于 NSFW(Not Safe For Work)Model,利用深度学习技术对色情图片进行识别与鉴黄。NSFW Model 是一种基于卷积神经网络(CNN)的图像识别模型,通过学习大量的色情图片和非色情图片,能够准确地判断一张图片是否含有色情内容。本项目在 NSFW Model 的基础上,进一步优化了模型结构,提高了识别的准确率和效率。 项目功能: 色情图片识别:用户上传图片后,系统会自动调用 NSFW Model 对图片进行识别,判断图片是否含有色情内容。如果含有色情内容,系统会给出相应的提示,并阻止图片的传播。 视频检测:针对网络视频,本项目采用帧提取技术,将视频分解为一帧帧图片,然后使用 NSFW Model 对这些图片进行识别。如果检测到含有色情内容的图片,系统会给出相应的提示,并阻止视频的传播。 实时监控:本项目可应用于网络直播、短视频平台等场景,实时监控画面内容,一旦检测到含有色情内容的画面,立即进行屏蔽处理,确保网络环境的纯洁。
### 如何在本地部署 NSFW 模型或服务 要在本地环境中成功部署 NSFW(不适宜工作场合内容)检测模型或服务,以下是详细的说明: #### 准备环境 为了确保能够顺利运行模型和服务,需要安装必要的依赖项。这些工具和库包括但不限于以下几类: - **Python 环境**: 推荐使用 Python 3.7 或更高版本。 - **Transformers 库**: 提供加载预训练模型的功能[^1]。 - **PyTorch/TensorFlow**: 支持深度学习框架的计算需求。 - **Pillow (PIL)**: 处理图像文件并将其转换为适合输入模型的形式。 具体命令如下所示: ```bash pip install transformers torch Pillow ``` #### 加载模型与测试 通过 Hugging Face 的 `transformers` 工具包可以直接访问已有的 NSFW 图片分类模型。例如,可以采用名为 `"Falconsai/nsfw_image_detection"` 的公开模型来完成此任务[^1]。 下面是一个简单的代码片段展示如何加载该模型并对单张图片执行预测操作: ```python from PIL import Image from transformers import pipeline def classify_nsfw(image_path): # 打开指定路径下的图片文件 img = Image.open(image_path) # 初始化 image-classification 流水线对象,并指明使用的特定模型名称 classifier = pipeline("image-classification", model="Falconsai/nsfw_image_detection") # 对传入的图片调用流水线方法得到其类别标签及其置信度分数列表形式的结果 result = classifier(img) return result if __name__ == "__main__": test_img_path = "<your_test_image>" output_results = classify_nsfw(test_img_path) print(output_results) ``` 注意替换 `<your_test_image>` 成实际存在的图片绝对或者相对地址字符串值之前再尝试运行以上脚本。 #### 构建 RESTful API 服务 如果希望进一步扩展功能至 Web 应用程序层面,则可考虑利用 Flask/Django 这样的轻量级 web 开发框架构建起支持 HTTP 请求交互的服务端接口。这里给出基于 FastAPI 实现的一个简单例子作为示范用途: ```python import uvicorn from fastapi import FastAPI, File, UploadFile from PIL import Image from io import BytesIO from typing import List from pydantic import BaseModel app = FastAPI() class Prediction(BaseModel): label: str score: float @app.post("/predict/", response_model=List[Prediction]) async def predict(file: UploadFile = File(...)): try: contents = await file.read() pil_image = Image.open(BytesIO(contents)) clf_pipeline = pipeline('image-classification', model='Falconsai/nsfw_image_detection') predictions = clf_pipeline(pil_image) formatted_preds = [{"label": pred['label'], "score": round(pred['score'], 4)} for pred in predictions] return formatted_preds except Exception as e: raise ValueError(f"Error processing uploaded file {e}") if __name__ == '__main__': uvicorn.run(app, host='0.0.0.0', port=8000) ``` 启动服务器之后即可向 `/predict/` 路径发送 POST 请求附带上传待分析的目标图片获取返回结果了。 ---
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